Overview

Brought to you by YData

Dataset statistics

Number of variables37
Number of observations164750
Missing cells473156
Missing cells (%)7.8%
Duplicate rows5
Duplicate rows (%)< 0.1%
Total size in memory225.1 MiB
Average record size in memory1.4 KiB

Variable types

Numeric11
Categorical17
Text8
DateTime1

Alerts

Dataset has 5 (< 0.1%) duplicate rowsDuplicates
is_super_over is highly imbalanced (99.4%) Imbalance
bye_runs is highly imbalanced (98.7%) Imbalance
noball_runs is highly imbalanced (98.4%) Imbalance
penalty_runs is highly imbalanced (> 99.9%) Imbalance
result is highly imbalanced (93.2%) Imbalance
dl_applied is highly imbalanced (86.2%) Imbalance
player_dismissed has 156593 (95.0%) missing values Missing
dismissal_kind has 156593 (95.0%) missing values Missing
fielder has 158832 (96.4%) missing values Missing
wide_runs has 159739 (97.0%) zeros Zeros
legbye_runs has 161989 (98.3%) zeros Zeros
batsman_runs has 65904 (40.0%) zeros Zeros
extra_runs has 155872 (94.6%) zeros Zeros
total_runs has 58061 (35.2%) zeros Zeros
win_by_runs has 88526 (53.7%) zeros Zeros
win_by_wickets has 78373 (47.6%) zeros Zeros

Reproduction

Analysis started2025-05-23 17:36:49.850849
Analysis finished2025-05-23 17:37:13.715977
Duration23.87 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

match_id
Real number (ℝ)

Distinct696
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean977.95176
Minimum1
Maximum7953
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2025-05-23T23:07:13.800902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35
Q1175
median349
Q3521
95-th percentile7919
Maximum7953
Range7952
Interquartile range (IQR)346

Descriptive statistics

Standard deviation2147.6718
Coefficient of variation (CV)2.1960918
Kurtosis6.501191
Mean977.95176
Median Absolute Deviation (MAD)173
Skewness2.901471
Sum1.6111755 × 108
Variance4612494.3
MonotonicityIncreasing
2025-05-23T23:07:13.925988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126 267
 
0.2%
34 263
 
0.2%
476 262
 
0.2%
534 262
 
0.2%
388 261
 
0.2%
190 259
 
0.2%
570 259
 
0.2%
536 258
 
0.2%
401 258
 
0.2%
257 257
 
0.2%
Other values (686) 162144
98.4%
ValueCountFrequency (%)
1 248
0.2%
2 247
0.1%
3 218
0.1%
4 247
0.1%
5 248
0.2%
6 216
0.1%
7 254
0.2%
8 212
0.1%
9 226
0.1%
10 239
0.1%
ValueCountFrequency (%)
7953 241
0.1%
7952 245
0.1%
7951 247
0.1%
7950 247
0.1%
7949 247
0.1%
7948 247
0.1%
7947 249
0.2%
7946 245
0.1%
7945 246
0.1%
7944 245
0.1%

inning
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 MiB
1
85409 
2
79260 
3
 
43
4
 
38

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters164750
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 85409
51.8%
2 79260
48.1%
3 43
 
< 0.1%
4 38
 
< 0.1%

Length

2025-05-23T23:07:14.031970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-23T23:07:14.129807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 85409
51.8%
2 79260
48.1%
3 43
 
< 0.1%
4 38
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 85409
51.8%
2 79260
48.1%
3 43
 
< 0.1%
4 38
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 164750
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 85409
51.8%
2 79260
48.1%
3 43
 
< 0.1%
4 38
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 164750
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 85409
51.8%
2 79260
48.1%
3 43
 
< 0.1%
4 38
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 164750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 85409
51.8%
2 79260
48.1%
3 43
 
< 0.1%
4 38
 
< 0.1%

batting_team
Categorical

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.0 MiB
Mumbai Indians
20673 
Royal Challengers Bangalore
19300 
Kings XI Punjab
19211 
Kolkata Knight Riders
19155 
Delhi Daredevils
18786 
Other values (8)
67625 

Length

Max length27
Median length21
Mean length17.994986
Min length13

Characters and Unicode

Total characters2964674
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSunrisers Hyderabad
2nd rowSunrisers Hyderabad
3rd rowSunrisers Hyderabad
4th rowSunrisers Hyderabad
5th rowSunrisers Hyderabad

Common Values

ValueCountFrequency (%)
Mumbai Indians 20673
12.5%
Royal Challengers Bangalore 19300
11.7%
Kings XI Punjab 19211
11.7%
Kolkata Knight Riders 19155
11.6%
Delhi Daredevils 18786
11.4%
Chennai Super Kings 17711
10.8%
Rajasthan Royals 15677
9.5%
Sunrisers Hyderabad 11132
6.8%
Deccan Chargers 9034
5.5%
Pune Warriors 5443
 
3.3%
Other values (3) 8628
5.2%

Length

2025-05-23T23:07:14.484957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kings 36922
 
9.0%
mumbai 20673
 
5.0%
indians 20673
 
5.0%
royal 19300
 
4.7%
challengers 19300
 
4.7%
bangalore 19300
 
4.7%
xi 19211
 
4.7%
punjab 19211
 
4.7%
kolkata 19155
 
4.7%
knight 19155
 
4.7%
Other values (20) 197039
48.1%

Most occurring characters

ValueCountFrequency (%)
a 334823
 
11.3%
n 245948
 
8.3%
245189
 
8.3%
e 224734
 
7.6%
i 204024
 
6.9%
s 196621
 
6.6%
r 172255
 
5.8%
l 151186
 
5.1%
g 110671
 
3.7%
h 101245
 
3.4%
Other values (27) 977978
33.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2290335
77.3%
Uppercase Letter 429150
 
14.5%
Space Separator 245189
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 334823
14.6%
n 245948
10.7%
e 224734
9.8%
i 204024
8.9%
s 196621
8.6%
r 172255
 
7.5%
l 151186
 
6.6%
g 110671
 
4.8%
h 101245
 
4.4%
u 86278
 
3.8%
Other values (11) 462550
20.2%
Uppercase Letter
ValueCountFrequency (%)
K 78396
18.3%
R 73289
17.1%
D 46606
10.9%
C 46045
10.7%
I 39884
9.3%
S 32323
7.5%
P 28134
 
6.6%
M 20673
 
4.8%
B 19300
 
4.5%
X 19211
 
4.5%
Other values (5) 25289
 
5.9%
Space Separator
ValueCountFrequency (%)
245189
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2719485
91.7%
Common 245189
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 334823
 
12.3%
n 245948
 
9.0%
e 224734
 
8.3%
i 204024
 
7.5%
s 196621
 
7.2%
r 172255
 
6.3%
l 151186
 
5.6%
g 110671
 
4.1%
h 101245
 
3.7%
u 86278
 
3.2%
Other values (26) 891700
32.8%
Common
ValueCountFrequency (%)
245189
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2964674
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 334823
 
11.3%
n 245948
 
8.3%
245189
 
8.3%
e 224734
 
7.6%
i 204024
 
6.9%
s 196621
 
6.6%
r 172255
 
5.8%
l 151186
 
5.1%
g 110671
 
3.7%
h 101245
 
3.4%
Other values (27) 977978
33.0%

bowling_team
Categorical

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.0 MiB
Mumbai Indians
20573 
Royal Challengers Bangalore
19627 
Kolkata Knight Riders
19290 
Kings XI Punjab
19055 
Delhi Daredevils
18725 
Other values (8)
67480 

Length

Max length27
Median length21
Mean length18.019818
Min length13

Characters and Unicode

Total characters2968765
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRoyal Challengers Bangalore
2nd rowRoyal Challengers Bangalore
3rd rowRoyal Challengers Bangalore
4th rowRoyal Challengers Bangalore
5th rowRoyal Challengers Bangalore

Common Values

ValueCountFrequency (%)
Mumbai Indians 20573
12.5%
Royal Challengers Bangalore 19627
11.9%
Kolkata Knight Riders 19290
11.7%
Kings XI Punjab 19055
11.6%
Delhi Daredevils 18725
11.4%
Chennai Super Kings 17533
10.6%
Rajasthan Royals 15813
9.6%
Sunrisers Hyderabad 10936
6.6%
Deccan Chargers 9039
5.5%
Pune Warriors 5457
 
3.3%
Other values (3) 8702
5.3%

Length

2025-05-23T23:07:14.634586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kings 36588
 
8.9%
mumbai 20573
 
5.0%
indians 20573
 
5.0%
royal 19627
 
4.8%
challengers 19627
 
4.8%
bangalore 19627
 
4.8%
kolkata 19290
 
4.7%
knight 19290
 
4.7%
riders 19290
 
4.7%
xi 19055
 
4.6%
Other values (20) 196622
47.9%

Most occurring characters

ValueCountFrequency (%)
a 336067
 
11.3%
n 245818
 
8.3%
245412
 
8.3%
e 225133
 
7.6%
i 203478
 
6.9%
s 196656
 
6.6%
r 172375
 
5.8%
l 152675
 
5.1%
g 111257
 
3.7%
h 101641
 
3.4%
Other values (27) 978253
33.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2294136
77.3%
Uppercase Letter 429217
 
14.5%
Space Separator 245412
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 336067
14.6%
n 245818
10.7%
e 225133
9.8%
i 203478
8.9%
s 196656
8.6%
r 172375
 
7.5%
l 152675
 
6.7%
g 111257
 
4.8%
h 101641
 
4.4%
u 85799
 
3.7%
Other values (11) 463237
20.2%
Uppercase Letter
ValueCountFrequency (%)
K 78396
18.3%
R 74086
17.3%
D 46489
10.8%
C 46199
10.8%
I 39628
9.2%
S 32012
7.5%
P 28055
 
6.5%
M 20573
 
4.8%
B 19627
 
4.6%
X 19055
 
4.4%
Other values (5) 25097
 
5.8%
Space Separator
ValueCountFrequency (%)
245412
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2723353
91.7%
Common 245412
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 336067
 
12.3%
n 245818
 
9.0%
e 225133
 
8.3%
i 203478
 
7.5%
s 196656
 
7.2%
r 172375
 
6.3%
l 152675
 
5.6%
g 111257
 
4.1%
h 101641
 
3.7%
u 85799
 
3.2%
Other values (26) 892454
32.8%
Common
ValueCountFrequency (%)
245412
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2968765
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 336067
 
11.3%
n 245818
 
8.3%
245412
 
8.3%
e 225133
 
7.6%
i 203478
 
6.9%
s 196656
 
6.6%
r 172375
 
5.8%
l 152675
 
5.1%
g 111257
 
3.7%
h 101641
 
3.4%
Other values (27) 978253
33.0%

over
Real number (ℝ)

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.151879
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2025-05-23T23:07:14.757615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q315
95-th percentile19
Maximum20
Range19
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.6756659
Coefficient of variation (CV)0.55907543
Kurtosis-1.1823057
Mean10.151879
Median Absolute Deviation (MAD)5
Skewness0.051671052
Sum1672522
Variance32.213183
MonotonicityNot monotonic
2025-05-23T23:07:14.869022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 8837
 
5.4%
2 8763
 
5.3%
3 8672
 
5.3%
4 8644
 
5.2%
5 8612
 
5.2%
6 8607
 
5.2%
7 8561
 
5.2%
8 8528
 
5.2%
9 8510
 
5.2%
10 8450
 
5.1%
Other values (10) 78566
47.7%
ValueCountFrequency (%)
1 8837
5.4%
2 8763
5.3%
3 8672
5.3%
4 8644
5.2%
5 8612
5.2%
6 8607
5.2%
7 8561
5.2%
8 8528
5.2%
9 8510
5.2%
10 8450
5.1%
ValueCountFrequency (%)
20 6186
3.8%
19 7197
4.4%
18 7692
4.7%
17 7941
4.8%
16 8040
4.9%
15 8170
5.0%
14 8245
5.0%
13 8339
5.1%
12 8365
5.1%
11 8391
5.1%

ball
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6162428
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2025-05-23T23:07:15.007192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile6
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8073977
Coefficient of variation (CV)0.49979988
Kurtosis-1.082362
Mean3.6162428
Median Absolute Deviation (MAD)2
Skewness0.096476015
Sum595776
Variance3.2666865
MonotonicityNot monotonic
2025-05-23T23:07:15.113031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 26715
16.2%
2 26640
16.2%
3 26567
16.1%
4 26504
16.1%
5 26419
16.0%
6 26328
16.0%
7 4728
 
2.9%
8 736
 
0.4%
9 113
 
0.1%
ValueCountFrequency (%)
1 26715
16.2%
2 26640
16.2%
3 26567
16.1%
4 26504
16.1%
5 26419
16.0%
6 26328
16.0%
7 4728
 
2.9%
8 736
 
0.4%
9 113
 
0.1%
ValueCountFrequency (%)
9 113
 
0.1%
8 736
 
0.4%
7 4728
 
2.9%
6 26328
16.0%
5 26419
16.0%
4 26504
16.1%
3 26567
16.1%
2 26640
16.2%
1 26715
16.2%
Distinct488
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.7 MiB
2025-05-23T23:07:15.408063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length20
Median length17
Mean length9.3613171
Min length5

Characters and Unicode

Total characters1542277
Distinct characters54
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowDA Warner
2nd rowDA Warner
3rd rowDA Warner
4th rowDA Warner
5th rowDA Warner
ValueCountFrequency (%)
v 5946
 
1.8%
s 5855
 
1.7%
singh 4602
 
1.4%
sr 4361
 
1.3%
sharma 4337
 
1.3%
m 4155
 
1.2%
da 4103
 
1.2%
sk 4003
 
1.2%
kohli 3899
 
1.2%
ms 3847
 
1.1%
Other values (665) 292189
86.6%
2025-05-23T23:07:15.836465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
172547
 
11.2%
a 170861
 
11.1%
i 75973
 
4.9%
h 70938
 
4.6%
n 70667
 
4.6%
r 66596
 
4.3%
e 62357
 
4.0%
S 61520
 
4.0%
l 57816
 
3.7%
M 40717
 
2.6%
Other values (44) 692285
44.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 897740
58.2%
Uppercase Letter 471773
30.6%
Space Separator 172547
 
11.2%
Dash Punctuation 217
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 170861
19.0%
i 75973
 
8.5%
h 70938
 
7.9%
n 70667
 
7.9%
r 66596
 
7.4%
e 62357
 
6.9%
l 57816
 
6.4%
s 40695
 
4.5%
t 33868
 
3.8%
o 33827
 
3.8%
Other values (16) 214142
23.9%
Uppercase Letter
ValueCountFrequency (%)
S 61520
13.0%
M 40717
 
8.6%
R 39727
 
8.4%
K 38090
 
8.1%
A 37607
 
8.0%
D 32596
 
6.9%
P 31279
 
6.6%
J 23192
 
4.9%
G 22642
 
4.8%
V 21643
 
4.6%
Other values (16) 122760
26.0%
Space Separator
ValueCountFrequency (%)
172547
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 217
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1369513
88.8%
Common 172764
 
11.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 170861
 
12.5%
i 75973
 
5.5%
h 70938
 
5.2%
n 70667
 
5.2%
r 66596
 
4.9%
e 62357
 
4.6%
S 61520
 
4.5%
l 57816
 
4.2%
M 40717
 
3.0%
s 40695
 
3.0%
Other values (42) 651373
47.6%
Common
ValueCountFrequency (%)
172547
99.9%
- 217
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1542277
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
172547
 
11.2%
a 170861
 
11.1%
i 75973
 
4.9%
h 70938
 
4.6%
n 70667
 
4.6%
r 66596
 
4.3%
e 62357
 
4.0%
S 61520
 
4.0%
l 57816
 
3.7%
M 40717
 
2.6%
Other values (44) 692285
44.9%
Distinct484
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.7 MiB
2025-05-23T23:07:16.167533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length20
Median length17
Mean length9.3623247
Min length5

Characters and Unicode

Total characters1542443
Distinct characters54
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowS Dhawan
2nd rowS Dhawan
3rd rowS Dhawan
4th rowS Dhawan
5th rowS Dhawan
ValueCountFrequency (%)
s 6049
 
1.8%
v 5910
 
1.8%
sr 4596
 
1.4%
sharma 4462
 
1.3%
singh 4309
 
1.3%
m 4306
 
1.3%
sk 4082
 
1.2%
am 3867
 
1.1%
da 3863
 
1.1%
raina 3832
 
1.1%
Other values (664) 292034
86.6%
2025-05-23T23:07:16.632143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
172560
 
11.2%
a 171933
 
11.1%
i 75692
 
4.9%
n 70689
 
4.6%
h 70495
 
4.6%
r 66608
 
4.3%
e 63304
 
4.1%
S 61426
 
4.0%
l 57286
 
3.7%
M 41467
 
2.7%
Other values (44) 690983
44.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 897911
58.2%
Uppercase Letter 471731
30.6%
Space Separator 172560
 
11.2%
Dash Punctuation 241
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 171933
19.1%
i 75692
 
8.4%
n 70689
 
7.9%
h 70495
 
7.9%
r 66608
 
7.4%
e 63304
 
7.1%
l 57286
 
6.4%
s 40625
 
4.5%
t 33171
 
3.7%
u 32669
 
3.6%
Other values (16) 215439
24.0%
Uppercase Letter
ValueCountFrequency (%)
S 61426
13.0%
M 41467
 
8.8%
R 39814
 
8.4%
K 37862
 
8.0%
A 37524
 
8.0%
D 31938
 
6.8%
P 30929
 
6.6%
J 23216
 
4.9%
G 22915
 
4.9%
V 21780
 
4.6%
Other values (16) 122860
26.0%
Space Separator
ValueCountFrequency (%)
172560
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 241
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1369642
88.8%
Common 172801
 
11.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 171933
 
12.6%
i 75692
 
5.5%
n 70689
 
5.2%
h 70495
 
5.1%
r 66608
 
4.9%
e 63304
 
4.6%
S 61426
 
4.5%
l 57286
 
4.2%
M 41467
 
3.0%
s 40625
 
3.0%
Other values (42) 650117
47.5%
Common
ValueCountFrequency (%)
172560
99.9%
- 241
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1542443
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
172560
 
11.2%
a 171933
 
11.1%
i 75692
 
4.9%
n 70689
 
4.6%
h 70495
 
4.6%
r 66608
 
4.3%
e 63304
 
4.1%
S 61426
 
4.0%
l 57286
 
3.7%
M 41467
 
2.7%
Other values (44) 690983
44.8%

bowler
Text

Distinct378
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.7 MiB
2025-05-23T23:07:16.924166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length17
Median length16
Mean length9.4861791
Min length5

Characters and Unicode

Total characters1562848
Distinct characters55
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowTS Mills
2nd rowTS Mills
3rd rowTS Mills
4th rowTS Mills
5th rowTS Mills
ValueCountFrequency (%)
r 9190
 
2.7%
singh 8897
 
2.7%
sharma 8589
 
2.6%
a 8208
 
2.4%
kumar 7194
 
2.1%
m 5737
 
1.7%
s 5581
 
1.7%
pp 4835
 
1.4%
p 4762
 
1.4%
b 3833
 
1.1%
Other values (536) 268894
80.1%
2025-05-23T23:07:17.312094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 198576
 
12.7%
170970
 
10.9%
n 84496
 
5.4%
r 81929
 
5.2%
h 77969
 
5.0%
i 68316
 
4.4%
e 66983
 
4.3%
S 61230
 
3.9%
l 51359
 
3.3%
M 43044
 
2.8%
Other values (45) 657976
42.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 950111
60.8%
Uppercase Letter 441106
28.2%
Space Separator 170970
 
10.9%
Dash Punctuation 586
 
< 0.1%
Open Punctuation 25
 
< 0.1%
Decimal Number 25
 
< 0.1%
Close Punctuation 25
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 198576
20.9%
n 84496
 
8.9%
r 81929
 
8.6%
h 77969
 
8.2%
i 68316
 
7.2%
e 66983
 
7.1%
l 51359
 
5.4%
t 37421
 
3.9%
o 36864
 
3.9%
m 34504
 
3.6%
Other values (16) 211694
22.3%
Uppercase Letter
ValueCountFrequency (%)
S 61230
13.9%
M 43044
9.8%
A 38852
 
8.8%
P 38478
 
8.7%
K 32410
 
7.3%
R 30769
 
7.0%
J 28747
 
6.5%
B 23660
 
5.4%
D 20653
 
4.7%
C 17312
 
3.9%
Other values (14) 105951
24.0%
Space Separator
ValueCountFrequency (%)
170970
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 586
100.0%
Open Punctuation
ValueCountFrequency (%)
( 25
100.0%
Decimal Number
ValueCountFrequency (%)
2 25
100.0%
Close Punctuation
ValueCountFrequency (%)
) 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1391217
89.0%
Common 171631
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 198576
 
14.3%
n 84496
 
6.1%
r 81929
 
5.9%
h 77969
 
5.6%
i 68316
 
4.9%
e 66983
 
4.8%
S 61230
 
4.4%
l 51359
 
3.7%
M 43044
 
3.1%
A 38852
 
2.8%
Other values (40) 618463
44.5%
Common
ValueCountFrequency (%)
170970
99.6%
- 586
 
0.3%
( 25
 
< 0.1%
2 25
 
< 0.1%
) 25
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1562848
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 198576
 
12.7%
170970
 
10.9%
n 84496
 
5.4%
r 81929
 
5.2%
h 77969
 
5.0%
i 68316
 
4.4%
e 66983
 
4.3%
S 61230
 
3.9%
l 51359
 
3.3%
M 43044
 
2.8%
Other values (45) 657976
42.1%

is_super_over
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 MiB
0
164669 
1
 
81

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters164750
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 164669
> 99.9%
1 81
 
< 0.1%

Length

2025-05-23T23:07:17.424968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-23T23:07:17.519950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 164669
> 99.9%
1 81
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 164669
> 99.9%
1 81
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 164750
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 164669
> 99.9%
1 81
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 164750
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 164669
> 99.9%
1 81
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 164750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 164669
> 99.9%
1 81
 
< 0.1%

wide_runs
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.037183612
Minimum0
Maximum5
Zeros159739
Zeros (%)97.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2025-05-23T23:07:17.599063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.25408681
Coefficient of variation (CV)6.833301
Kurtosis190.26192
Mean0.037183612
Median Absolute Deviation (MAD)0
Skewness11.653352
Sum6126
Variance0.064560108
MonotonicityNot monotonic
2025-05-23T23:07:17.698637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 159739
97.0%
1 4546
 
2.8%
2 219
 
0.1%
5 200
 
0.1%
3 42
 
< 0.1%
4 4
 
< 0.1%
ValueCountFrequency (%)
0 159739
97.0%
1 4546
 
2.8%
2 219
 
0.1%
3 42
 
< 0.1%
4 4
 
< 0.1%
5 200
 
0.1%
ValueCountFrequency (%)
5 200
 
0.1%
4 4
 
< 0.1%
3 42
 
< 0.1%
2 219
 
0.1%
1 4546
 
2.8%
0 159739
97.0%

bye_runs
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 MiB
0
164302 
1
 
309
4
 
109
2
 
28
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters164750
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 164302
99.7%
1 309
 
0.2%
4 109
 
0.1%
2 28
 
< 0.1%
3 2
 
< 0.1%

Length

2025-05-23T23:07:17.804597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-23T23:07:17.899926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 164302
99.7%
1 309
 
0.2%
4 109
 
0.1%
2 28
 
< 0.1%
3 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 164302
99.7%
1 309
 
0.2%
4 109
 
0.1%
2 28
 
< 0.1%
3 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 164750
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 164302
99.7%
1 309
 
0.2%
4 109
 
0.1%
2 28
 
< 0.1%
3 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 164750
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 164302
99.7%
1 309
 
0.2%
4 109
 
0.1%
2 28
 
< 0.1%
3 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 164750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 164302
99.7%
1 309
 
0.2%
4 109
 
0.1%
2 28
 
< 0.1%
3 2
 
< 0.1%

legbye_runs
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0215478
Minimum0
Maximum5
Zeros161989
Zeros (%)98.3%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2025-05-23T23:07:17.979683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.19641002
Coefficient of variation (CV)9.1150849
Kurtosis237.68377
Mean0.0215478
Median Absolute Deviation (MAD)0
Skewness13.631276
Sum3550
Variance0.038576898
MonotonicityNot monotonic
2025-05-23T23:07:18.058464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 161989
98.3%
1 2408
 
1.5%
4 204
 
0.1%
2 129
 
0.1%
3 16
 
< 0.1%
5 4
 
< 0.1%
ValueCountFrequency (%)
0 161989
98.3%
1 2408
 
1.5%
2 129
 
0.1%
3 16
 
< 0.1%
4 204
 
0.1%
5 4
 
< 0.1%
ValueCountFrequency (%)
5 4
 
< 0.1%
4 204
 
0.1%
3 16
 
< 0.1%
2 129
 
0.1%
1 2408
 
1.5%
0 161989
98.3%

noball_runs
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 MiB
0
164093 
1
 
641
2
 
9
5
 
6
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters164750
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 164093
99.6%
1 641
 
0.4%
2 9
 
< 0.1%
5 6
 
< 0.1%
3 1
 
< 0.1%

Length

2025-05-23T23:07:18.155653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-23T23:07:18.265058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 164093
99.6%
1 641
 
0.4%
2 9
 
< 0.1%
5 6
 
< 0.1%
3 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 164093
99.6%
1 641
 
0.4%
2 9
 
< 0.1%
5 6
 
< 0.1%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 164750
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 164093
99.6%
1 641
 
0.4%
2 9
 
< 0.1%
5 6
 
< 0.1%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 164750
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 164093
99.6%
1 641
 
0.4%
2 9
 
< 0.1%
5 6
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 164750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 164093
99.6%
1 641
 
0.4%
2 9
 
< 0.1%
5 6
 
< 0.1%
3 1
 
< 0.1%

penalty_runs
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 MiB
0
164748 
5
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters164750
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 164748
> 99.9%
5 2
 
< 0.1%

Length

2025-05-23T23:07:18.360559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-23T23:07:18.467923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 164748
> 99.9%
5 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 164748
> 99.9%
5 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 164750
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 164748
> 99.9%
5 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 164750
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 164748
> 99.9%
5 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 164750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 164748
> 99.9%
5 2
 
< 0.1%

batsman_runs
Real number (ℝ)

Zeros 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2372382
Minimum0
Maximum7
Zeros65904
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2025-05-23T23:07:18.536305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile4
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6035112
Coefficient of variation (CV)1.2960407
Kurtosis1.6571485
Mean1.2372382
Median Absolute Deviation (MAD)1
Skewness1.5884311
Sum203835
Variance2.571248
MonotonicityNot monotonic
2025-05-23T23:07:18.614733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 65904
40.0%
1 61580
37.4%
4 18707
 
11.4%
2 10560
 
6.4%
6 7392
 
4.5%
3 543
 
0.3%
5 61
 
< 0.1%
7 3
 
< 0.1%
ValueCountFrequency (%)
0 65904
40.0%
1 61580
37.4%
2 10560
 
6.4%
3 543
 
0.3%
4 18707
 
11.4%
5 61
 
< 0.1%
6 7392
 
4.5%
7 3
 
< 0.1%
ValueCountFrequency (%)
7 3
 
< 0.1%
6 7392
 
4.5%
5 61
 
< 0.1%
4 18707
 
11.4%
3 543
 
0.3%
2 10560
 
6.4%
1 61580
37.4%
0 65904
40.0%

extra_runs
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.067890744
Minimum0
Maximum7
Zeros155872
Zeros (%)94.6%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2025-05-23T23:07:18.704153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.34514417
Coefficient of variation (CV)5.0838178
Kurtosis90.582081
Mean0.067890744
Median Absolute Deviation (MAD)0
Skewness8.2054299
Sum11185
Variance0.1191245
MonotonicityNot monotonic
2025-05-23T23:07:18.773587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 155872
94.6%
1 7904
 
4.8%
2 384
 
0.2%
4 317
 
0.2%
5 211
 
0.1%
3 61
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 155872
94.6%
1 7904
 
4.8%
2 384
 
0.2%
3 61
 
< 0.1%
4 317
 
0.2%
5 211
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
5 211
 
0.1%
4 317
 
0.2%
3 61
 
< 0.1%
2 384
 
0.2%
1 7904
 
4.8%
0 155872
94.6%

total_runs
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.305129
Minimum0
Maximum10
Zeros58061
Zeros (%)35.2%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2025-05-23T23:07:18.864919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile4
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5962546
Coefficient of variation (CV)1.2230627
Kurtosis1.626415
Mean1.305129
Median Absolute Deviation (MAD)1
Skewness1.558686
Sum215020
Variance2.5480286
MonotonicityNot monotonic
2025-05-23T23:07:18.947535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 67672
41.1%
0 58061
35.2%
4 18914
 
11.5%
2 11696
 
7.1%
6 7360
 
4.5%
3 643
 
0.4%
5 333
 
0.2%
7 38
 
< 0.1%
8 25
 
< 0.1%
10 8
 
< 0.1%
ValueCountFrequency (%)
0 58061
35.2%
1 67672
41.1%
2 11696
 
7.1%
3 643
 
0.4%
4 18914
 
11.5%
5 333
 
0.2%
6 7360
 
4.5%
7 38
 
< 0.1%
8 25
 
< 0.1%
10 8
 
< 0.1%
ValueCountFrequency (%)
10 8
 
< 0.1%
8 25
 
< 0.1%
7 38
 
< 0.1%
6 7360
 
4.5%
5 333
 
0.2%
4 18914
 
11.5%
3 643
 
0.4%
2 11696
 
7.1%
1 67672
41.1%
0 58061
35.2%

player_dismissed
Text

Missing 

Distinct464
Distinct (%)5.7%
Missing156593
Missing (%)95.0%
Memory size6.6 MiB
2025-05-23T23:07:19.201923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length20
Median length17
Mean length9.3964693
Min length5

Characters and Unicode

Total characters76647
Distinct characters54
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78 ?
Unique (%)1.0%

Sample

1st rowDA Warner
2nd rowS Dhawan
3rd rowMC Henriques
4th rowYuvraj Singh
5th rowMandeep Singh
ValueCountFrequency (%)
singh 301
 
1.8%
s 264
 
1.6%
v 235
 
1.4%
r 231
 
1.4%
m 226
 
1.4%
sharma 221
 
1.3%
sk 173
 
1.0%
sr 168
 
1.0%
patel 168
 
1.0%
pathan 165
 
1.0%
Other values (636) 14546
87.1%
2025-05-23T23:07:19.582750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 8717
 
11.4%
8541
 
11.1%
i 3693
 
4.8%
h 3628
 
4.7%
n 3523
 
4.6%
r 3376
 
4.4%
e 3099
 
4.0%
S 2996
 
3.9%
l 2741
 
3.6%
M 2023
 
2.6%
Other values (44) 34310
44.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 44914
58.6%
Uppercase Letter 23170
30.2%
Space Separator 8541
 
11.1%
Dash Punctuation 22
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 8717
19.4%
i 3693
 
8.2%
h 3628
 
8.1%
n 3523
 
7.8%
r 3376
 
7.5%
e 3099
 
6.9%
l 2741
 
6.1%
s 1911
 
4.3%
t 1758
 
3.9%
o 1696
 
3.8%
Other values (16) 10772
24.0%
Uppercase Letter
ValueCountFrequency (%)
S 2996
12.9%
M 2023
 
8.7%
A 1936
 
8.4%
R 1925
 
8.3%
K 1787
 
7.7%
P 1643
 
7.1%
D 1464
 
6.3%
J 1198
 
5.2%
V 1021
 
4.4%
G 1020
 
4.4%
Other values (16) 6157
26.6%
Space Separator
ValueCountFrequency (%)
8541
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 68084
88.8%
Common 8563
 
11.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 8717
 
12.8%
i 3693
 
5.4%
h 3628
 
5.3%
n 3523
 
5.2%
r 3376
 
5.0%
e 3099
 
4.6%
S 2996
 
4.4%
l 2741
 
4.0%
M 2023
 
3.0%
A 1936
 
2.8%
Other values (42) 32352
47.5%
Common
ValueCountFrequency (%)
8541
99.7%
- 22
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 76647
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 8717
 
11.4%
8541
 
11.1%
i 3693
 
4.8%
h 3628
 
4.7%
n 3523
 
4.6%
r 3376
 
4.4%
e 3099
 
4.0%
S 2996
 
3.9%
l 2741
 
3.6%
M 2023
 
2.6%
Other values (44) 34310
44.8%

dismissal_kind
Categorical

Missing 

Distinct9
Distinct (%)0.1%
Missing156593
Missing (%)95.0%
Memory size11.3 MiB
caught
4861 
bowled
1495 
run out
813 
lbw
494 
stumped
 
262
Other values (4)
 
232

Length

Max length21
Median length6
Mean length6.2489886
Min length3

Characters and Unicode

Total characters50973
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowcaught
2nd rowcaught
3rd rowcaught
4th rowbowled
5th rowbowled

Common Values

ValueCountFrequency (%)
caught 4861
 
3.0%
bowled 1495
 
0.9%
run out 813
 
0.5%
lbw 494
 
0.3%
stumped 262
 
0.2%
caught and bowled 211
 
0.1%
retired hurt 11
 
< 0.1%
hit wicket 9
 
< 0.1%
obstructing the field 1
 
< 0.1%
(Missing) 156593
95.0%

Length

2025-05-23T23:07:19.693928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-23T23:07:19.819608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
caught 5072
53.9%
bowled 1706
 
18.1%
run 813
 
8.6%
out 813
 
8.6%
lbw 494
 
5.2%
stumped 262
 
2.8%
and 211
 
2.2%
retired 11
 
0.1%
hurt 11
 
0.1%
hit 9
 
0.1%
Other values (4) 12
 
0.1%

Most occurring characters

ValueCountFrequency (%)
u 6972
13.7%
t 6190
12.1%
a 5283
10.4%
h 5093
10.0%
c 5082
10.0%
g 5073
10.0%
o 2520
 
4.9%
w 2209
 
4.3%
b 2201
 
4.3%
l 2201
 
4.3%
Other values (11) 8149
16.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 49716
97.5%
Space Separator 1257
 
2.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 6972
14.0%
t 6190
12.5%
a 5283
10.6%
h 5093
10.2%
c 5082
10.2%
g 5073
10.2%
o 2520
 
5.1%
w 2209
 
4.4%
b 2201
 
4.4%
l 2201
 
4.4%
Other values (10) 6892
13.9%
Space Separator
ValueCountFrequency (%)
1257
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 49716
97.5%
Common 1257
 
2.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 6972
14.0%
t 6190
12.5%
a 5283
10.6%
h 5093
10.2%
c 5082
10.2%
g 5073
10.2%
o 2520
 
5.1%
w 2209
 
4.4%
b 2201
 
4.4%
l 2201
 
4.4%
Other values (10) 6892
13.9%
Common
ValueCountFrequency (%)
1257
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 6972
13.7%
t 6190
12.1%
a 5283
10.4%
h 5093
10.0%
c 5082
10.0%
g 5073
10.0%
o 2520
 
4.9%
w 2209
 
4.3%
b 2201
 
4.3%
l 2201
 
4.3%
Other values (11) 8149
16.0%

fielder
Text

Missing 

Distinct476
Distinct (%)8.0%
Missing158832
Missing (%)96.4%
Memory size6.5 MiB
2025-05-23T23:07:20.154504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length21
Median length19
Mean length9.4979723
Min length5

Characters and Unicode

Total characters56209
Distinct characters55
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84 ?
Unique (%)1.4%

Sample

1st rowMandeep Singh
2nd rowSachin Baby
3rd rowSachin Baby
4th rowDA Warner
5th rowBCJ Cutting
ValueCountFrequency (%)
singh 191
 
1.6%
m 184
 
1.5%
r 181
 
1.5%
sharma 180
 
1.5%
ms 177
 
1.4%
karthik 159
 
1.3%
s 159
 
1.3%
patel 145
 
1.2%
kd 145
 
1.2%
de 143
 
1.2%
Other values (599) 10560
86.4%
2025-05-23T23:07:20.582675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 6428
 
11.4%
6306
 
11.2%
i 2827
 
5.0%
h 2760
 
4.9%
n 2511
 
4.5%
r 2478
 
4.4%
e 2218
 
3.9%
S 2151
 
3.8%
l 1990
 
3.5%
K 1474
 
2.6%
Other values (45) 25066
44.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 32983
58.7%
Uppercase Letter 16757
29.8%
Space Separator 6306
 
11.2%
Open Punctuation 76
 
0.1%
Close Punctuation 76
 
0.1%
Dash Punctuation 11
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6428
19.5%
i 2827
 
8.6%
h 2760
 
8.4%
n 2511
 
7.6%
r 2478
 
7.5%
e 2218
 
6.7%
l 1990
 
6.0%
t 1417
 
4.3%
s 1394
 
4.2%
o 1265
 
3.8%
Other values (16) 7695
23.3%
Uppercase Letter
ValueCountFrequency (%)
S 2151
12.8%
K 1474
 
8.8%
M 1471
 
8.8%
A 1400
 
8.4%
R 1319
 
7.9%
P 1229
 
7.3%
D 1158
 
6.9%
J 848
 
5.1%
B 801
 
4.8%
V 749
 
4.5%
Other values (15) 4157
24.8%
Space Separator
ValueCountFrequency (%)
6306
100.0%
Open Punctuation
ValueCountFrequency (%)
( 76
100.0%
Close Punctuation
ValueCountFrequency (%)
) 76
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 49740
88.5%
Common 6469
 
11.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6428
 
12.9%
i 2827
 
5.7%
h 2760
 
5.5%
n 2511
 
5.0%
r 2478
 
5.0%
e 2218
 
4.5%
S 2151
 
4.3%
l 1990
 
4.0%
K 1474
 
3.0%
M 1471
 
3.0%
Other values (41) 23432
47.1%
Common
ValueCountFrequency (%)
6306
97.5%
( 76
 
1.2%
) 76
 
1.2%
- 11
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56209
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6428
 
11.4%
6306
 
11.2%
i 2827
 
5.0%
h 2760
 
4.9%
n 2511
 
4.5%
r 2478
 
4.4%
e 2218
 
3.9%
S 2151
 
3.8%
l 1990
 
3.5%
K 1474
 
2.6%
Other values (45) 25066
44.6%

season
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.9614
Minimum2008
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2025-05-23T23:07:20.693140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2008
5-th percentile2008
Q12010
median2013
Q32016
95-th percentile2018
Maximum2018
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.0631189
Coefficient of variation (CV)0.0015216978
Kurtosis-1.1206017
Mean2012.9614
Median Absolute Deviation (MAD)3
Skewness0.053749855
Sum3.3163538 × 108
Variance9.3826974
MonotonicityNot monotonic
2025-05-23T23:07:20.804312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2013 18177
11.0%
2012 17767
10.8%
2011 17013
10.3%
2010 14498
8.8%
2014 14300
8.7%
2018 14290
8.7%
2016 14096
8.6%
2017 13862
8.4%
2015 13652
8.3%
2009 13606
8.3%
ValueCountFrequency (%)
2008 13489
8.2%
2009 13606
8.3%
2010 14498
8.8%
2011 17013
10.3%
2012 17767
10.8%
2013 18177
11.0%
2014 14300
8.7%
2015 13652
8.3%
2016 14096
8.6%
2017 13862
8.4%
ValueCountFrequency (%)
2018 14290
8.7%
2017 13862
8.4%
2016 14096
8.6%
2015 13652
8.3%
2014 14300
8.7%
2013 18177
11.0%
2012 17767
10.8%
2011 17013
10.3%
2010 14498
8.8%
2009 13606
8.3%

city
Categorical

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.3 MiB
Mumbai
22591 
Bengaluru
16872 
Kolkata
16268 
Delhi
15604 
Hyderabad
13303 
Other values (26)
80112 

Length

Max length14
Median length13
Mean length7.0456388
Min length4

Characters and Unicode

Total characters1160769
Distinct characters41
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHyderabad
2nd rowHyderabad
3rd rowHyderabad
4th rowHyderabad
5th rowHyderabad

Common Values

ValueCountFrequency (%)
Mumbai 22591
13.7%
Bengaluru 16872
10.2%
Kolkata 16268
9.9%
Delhi 15604
9.5%
Hyderabad 13303
 
8.1%
Chennai 12006
 
7.3%
Mohali 11581
 
7.0%
Jaipur 9478
 
5.8%
Pune 9119
 
5.5%
Durban 3643
 
2.2%
Other values (21) 34285
20.8%

Length

2025-05-23T23:07:20.900286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mumbai 22591
13.3%
bengaluru 16872
9.9%
kolkata 16268
9.6%
delhi 15604
 
9.2%
hyderabad 13303
 
7.8%
chennai 12006
 
7.0%
mohali 11581
 
6.8%
jaipur 9478
 
5.6%
pune 9119
 
5.4%
durban 3643
 
2.1%
Other values (25) 39840
23.4%

Most occurring characters

ValueCountFrequency (%)
a 180833
15.6%
u 91494
 
7.9%
i 87156
 
7.5%
e 82236
 
7.1%
n 74330
 
6.4%
l 65387
 
5.6%
r 59162
 
5.1%
h 57734
 
5.0%
b 51721
 
4.5%
o 43802
 
3.8%
Other values (31) 366914
31.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 984909
84.8%
Uppercase Letter 170305
 
14.7%
Space Separator 5555
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 180833
18.4%
u 91494
9.3%
i 87156
8.8%
e 82236
8.3%
n 74330
 
7.5%
l 65387
 
6.6%
r 59162
 
6.0%
h 57734
 
5.9%
b 51721
 
5.3%
o 43802
 
4.4%
Other values (13) 191054
19.4%
Uppercase Letter
ValueCountFrequency (%)
M 34172
20.1%
D 24730
14.5%
K 19070
11.2%
C 18106
10.6%
B 17372
10.2%
H 13303
 
7.8%
J 11418
 
6.7%
P 10796
 
6.3%
R 5483
 
3.2%
A 4517
 
2.7%
Other values (7) 11338
 
6.7%
Space Separator
ValueCountFrequency (%)
5555
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1155214
99.5%
Common 5555
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 180833
15.7%
u 91494
 
7.9%
i 87156
 
7.5%
e 82236
 
7.1%
n 74330
 
6.4%
l 65387
 
5.7%
r 59162
 
5.1%
h 57734
 
5.0%
b 51721
 
4.5%
o 43802
 
3.8%
Other values (30) 361359
31.3%
Common
ValueCountFrequency (%)
5555
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1160769
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 180833
15.6%
u 91494
 
7.9%
i 87156
 
7.5%
e 82236
 
7.1%
n 74330
 
6.4%
l 65387
 
5.6%
r 59162
 
5.1%
h 57734
 
5.0%
b 51721
 
4.5%
o 43802
 
3.8%
Other values (31) 366914
31.6%

date
Date

Distinct498
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
Minimum2008-04-18 00:00:00
Maximum2018-12-05 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-23T23:07:20.998549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:21.105243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

team1
Categorical

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.0 MiB
Mumbai Indians
22019 
Chennai Super Kings
19891 
Kings XI Punjab
19754 
Royal Challengers Bangalore
18014 
Kolkata Knight Riders
17221 
Other values (8)
67851 

Length

Max length27
Median length21
Mean length17.918634
Min length13

Characters and Unicode

Total characters2952095
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSunrisers Hyderabad
2nd rowSunrisers Hyderabad
3rd rowSunrisers Hyderabad
4th rowSunrisers Hyderabad
5th rowSunrisers Hyderabad

Common Values

ValueCountFrequency (%)
Mumbai Indians 22019
13.4%
Chennai Super Kings 19891
12.1%
Kings XI Punjab 19754
12.0%
Royal Challengers Bangalore 18014
10.9%
Kolkata Knight Riders 17221
10.5%
Delhi Daredevils 16559
10.1%
Rajasthan Royals 14677
8.9%
Sunrisers Hyderabad 13010
7.9%
Deccan Chargers 10335
6.3%
Pune Warriors 4759
 
2.9%
Other values (3) 8511
 
5.2%

Length

2025-05-23T23:07:21.241658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kings 39645
 
9.7%
mumbai 22019
 
5.4%
indians 22019
 
5.4%
chennai 19891
 
4.9%
super 19891
 
4.9%
xi 19754
 
4.8%
punjab 19754
 
4.8%
royal 18014
 
4.4%
challengers 18014
 
4.4%
bangalore 18014
 
4.4%
Other values (20) 192567
47.0%

Most occurring characters

ValueCountFrequency (%)
a 330367
 
11.2%
n 253214
 
8.6%
244832
 
8.3%
e 222575
 
7.5%
i 204518
 
6.9%
s 197639
 
6.7%
r 173837
 
5.9%
l 138722
 
4.7%
g 110333
 
3.7%
h 98347
 
3.3%
Other values (27) 977711
33.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2277927
77.2%
Uppercase Letter 429336
 
14.5%
Space Separator 244832
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 330367
14.5%
n 253214
11.1%
e 222575
9.8%
i 204518
9.0%
s 197639
8.7%
r 173837
 
7.6%
l 138722
 
6.1%
g 110333
 
4.8%
h 98347
 
4.3%
u 91496
 
4.0%
Other values (11) 456879
20.1%
Uppercase Letter
ValueCountFrequency (%)
K 77387
18.0%
R 68141
15.9%
C 48240
11.2%
D 43453
10.1%
I 41773
9.7%
S 36453
8.5%
P 28065
 
6.5%
M 22019
 
5.1%
X 19754
 
4.6%
B 18014
 
4.2%
Other values (5) 26037
 
6.1%
Space Separator
ValueCountFrequency (%)
244832
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2707263
91.7%
Common 244832
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 330367
 
12.2%
n 253214
 
9.4%
e 222575
 
8.2%
i 204518
 
7.6%
s 197639
 
7.3%
r 173837
 
6.4%
l 138722
 
5.1%
g 110333
 
4.1%
h 98347
 
3.6%
u 91496
 
3.4%
Other values (26) 886215
32.7%
Common
ValueCountFrequency (%)
244832
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2952095
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 330367
 
11.2%
n 253214
 
8.6%
244832
 
8.3%
e 222575
 
7.5%
i 204518
 
6.9%
s 197639
 
6.7%
r 173837
 
5.9%
l 138722
 
4.7%
g 110333
 
3.7%
h 98347
 
3.3%
Other values (27) 977711
33.1%

team2
Categorical

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.1 MiB
Kolkata Knight Riders
21224 
Delhi Daredevils
20952 
Royal Challengers Bangalore
20913 
Mumbai Indians
19227 
Kings XI Punjab
18512 
Other values (8)
63922 

Length

Max length27
Median length21
Mean length18.09617
Min length13

Characters and Unicode

Total characters2981344
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRoyal Challengers Bangalore
2nd rowRoyal Challengers Bangalore
3rd rowRoyal Challengers Bangalore
4th rowRoyal Challengers Bangalore
5th rowRoyal Challengers Bangalore

Common Values

ValueCountFrequency (%)
Kolkata Knight Riders 21224
12.9%
Delhi Daredevils 20952
12.7%
Royal Challengers Bangalore 20913
12.7%
Mumbai Indians 19227
11.7%
Kings XI Punjab 18512
11.2%
Rajasthan Royals 16813
10.2%
Chennai Super Kings 15353
9.3%
Sunrisers Hyderabad 9058
5.5%
Deccan Chargers 7738
 
4.7%
Pune Warriors 6141
 
3.7%
Other values (3) 8819
5.4%

Length

2025-05-23T23:07:21.341772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kings 33865
 
8.2%
kolkata 21224
 
5.2%
riders 21224
 
5.2%
knight 21224
 
5.2%
delhi 20952
 
5.1%
daredevils 20952
 
5.1%
royal 20913
 
5.1%
challengers 20913
 
5.1%
bangalore 20913
 
5.1%
mumbai 19227
 
4.7%
Other values (20) 189112
46.1%

Most occurring characters

ValueCountFrequency (%)
a 340523
 
11.4%
245769
 
8.2%
n 238552
 
8.0%
e 227292
 
7.6%
i 202984
 
6.8%
s 195638
 
6.6%
r 170793
 
5.7%
l 165139
 
5.5%
g 111595
 
3.7%
h 104539
 
3.5%
Other values (27) 978520
32.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2306544
77.4%
Uppercase Letter 429031
 
14.4%
Space Separator 245769
 
8.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 340523
14.8%
n 238552
10.3%
e 227292
9.9%
i 202984
8.8%
s 195638
8.5%
r 170793
 
7.4%
l 165139
 
7.2%
g 111595
 
4.8%
h 104539
 
4.5%
o 91352
 
4.0%
Other values (11) 458137
19.9%
Uppercase Letter
ValueCountFrequency (%)
K 79405
18.5%
R 79234
18.5%
D 49642
11.6%
C 44004
10.3%
I 37739
8.8%
P 28124
 
6.6%
S 27882
 
6.5%
B 20913
 
4.9%
M 19227
 
4.5%
X 18512
 
4.3%
Other values (5) 24349
 
5.7%
Space Separator
ValueCountFrequency (%)
245769
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2735575
91.8%
Common 245769
 
8.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 340523
 
12.4%
n 238552
 
8.7%
e 227292
 
8.3%
i 202984
 
7.4%
s 195638
 
7.2%
r 170793
 
6.2%
l 165139
 
6.0%
g 111595
 
4.1%
h 104539
 
3.8%
o 91352
 
3.3%
Other values (26) 887168
32.4%
Common
ValueCountFrequency (%)
245769
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2981344
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 340523
 
11.4%
245769
 
8.2%
n 238552
 
8.0%
e 227292
 
7.6%
i 202984
 
6.8%
s 195638
 
6.6%
r 170793
 
5.7%
l 165139
 
5.5%
g 111595
 
3.7%
h 104539
 
3.5%
Other values (27) 978520
32.8%

toss_winner
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.0 MiB
Mumbai Indians
21577 
Kolkata Knight Riders
20563 
Delhi Daredevils
18548 
Chennai Super Kings
18457 
Royal Challengers Bangalore
18019 
Other values (9)
67586 

Length

Max length27
Median length22
Mean length17.913627
Min length13

Characters and Unicode

Total characters2951270
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRoyal Challengers Bangalore
2nd rowRoyal Challengers Bangalore
3rd rowRoyal Challengers Bangalore
4th rowRoyal Challengers Bangalore
5th rowRoyal Challengers Bangalore

Common Values

ValueCountFrequency (%)
Mumbai Indians 21577
13.1%
Kolkata Knight Riders 20563
12.5%
Delhi Daredevils 18548
11.3%
Chennai Super Kings 18457
11.2%
Royal Challengers Bangalore 18019
10.9%
Kings XI Punjab 17873
10.8%
Rajasthan Royals 16438
10.0%
Deccan Chargers 10376
6.3%
Sunrisers Hyderabad 9881
6.0%
Pune Warriors 4798
 
2.9%
Other values (4) 8220
 
5.0%

Length

2025-05-23T23:07:21.442091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kings 36330
 
8.9%
mumbai 21577
 
5.3%
indians 21577
 
5.3%
kolkata 20563
 
5.0%
knight 20563
 
5.0%
riders 20563
 
5.0%
delhi 18548
 
4.5%
daredevils 18548
 
4.5%
chennai 18457
 
4.5%
super 18457
 
4.5%
Other values (21) 193863
47.4%

Most occurring characters

ValueCountFrequency (%)
a 335864
 
11.4%
n 244573
 
8.3%
244296
 
8.3%
e 221758
 
7.5%
i 204810
 
6.9%
s 194332
 
6.6%
r 168375
 
5.7%
l 147933
 
5.0%
g 109055
 
3.7%
h 104161
 
3.5%
Other values (27) 976113
33.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2280055
77.3%
Uppercase Letter 426919
 
14.5%
Space Separator 244296
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 335864
14.7%
n 244573
10.7%
e 221758
9.7%
i 204810
9.0%
s 194332
8.5%
r 168375
 
7.4%
l 147933
 
6.5%
g 109055
 
4.8%
h 104161
 
4.6%
u 83680
 
3.7%
Other values (11) 465514
20.4%
Uppercase Letter
ValueCountFrequency (%)
K 80976
19.0%
R 74332
17.4%
D 47472
11.1%
C 46852
11.0%
I 39450
9.2%
S 31212
 
7.3%
P 25545
 
6.0%
M 21577
 
5.1%
B 18019
 
4.2%
X 17873
 
4.2%
Other values (5) 23611
 
5.5%
Space Separator
ValueCountFrequency (%)
244296
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2706974
91.7%
Common 244296
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 335864
 
12.4%
n 244573
 
9.0%
e 221758
 
8.2%
i 204810
 
7.6%
s 194332
 
7.2%
r 168375
 
6.2%
l 147933
 
5.5%
g 109055
 
4.0%
h 104161
 
3.8%
u 83680
 
3.1%
Other values (26) 892433
33.0%
Common
ValueCountFrequency (%)
244296
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2951270
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 335864
 
11.4%
n 244573
 
8.3%
244296
 
8.3%
e 221758
 
7.5%
i 204810
 
6.9%
s 194332
 
6.6%
r 168375
 
5.7%
l 147933
 
5.0%
g 109055
 
3.7%
h 104161
 
3.5%
Other values (27) 976113
33.1%

toss_decision
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.9 MiB
field
97308 
bat
67442 

Length

Max length5
Median length5
Mean length4.1812807
Min length3

Characters and Unicode

Total characters688866
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfield
2nd rowfield
3rd rowfield
4th rowfield
5th rowfield

Common Values

ValueCountFrequency (%)
field 97308
59.1%
bat 67442
40.9%

Length

2025-05-23T23:07:21.535174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-23T23:07:21.646080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
field 97308
59.1%
bat 67442
40.9%

Most occurring characters

ValueCountFrequency (%)
f 97308
14.1%
i 97308
14.1%
e 97308
14.1%
l 97308
14.1%
d 97308
14.1%
b 67442
9.8%
a 67442
9.8%
t 67442
9.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 688866
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 97308
14.1%
i 97308
14.1%
e 97308
14.1%
l 97308
14.1%
d 97308
14.1%
b 67442
9.8%
a 67442
9.8%
t 67442
9.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 688866
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 97308
14.1%
i 97308
14.1%
e 97308
14.1%
l 97308
14.1%
d 97308
14.1%
b 67442
9.8%
a 67442
9.8%
t 67442
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 688866
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 97308
14.1%
i 97308
14.1%
e 97308
14.1%
l 97308
14.1%
d 97308
14.1%
b 67442
9.8%
a 67442
9.8%
t 67442
9.8%

result
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.2 MiB
normal
162601 
tie
 
1828
no result
 
321

Length

Max length9
Median length6
Mean length5.9725584
Min length3

Characters and Unicode

Total characters983979
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rownormal
3rd rownormal
4th rownormal
5th rownormal

Common Values

ValueCountFrequency (%)
normal 162601
98.7%
tie 1828
 
1.1%
no result 321
 
0.2%

Length

2025-05-23T23:07:21.733034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-23T23:07:21.836341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
normal 162601
98.5%
tie 1828
 
1.1%
no 321
 
0.2%
result 321
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n 162922
16.6%
o 162922
16.6%
r 162922
16.6%
l 162922
16.6%
m 162601
16.5%
a 162601
16.5%
t 2149
 
0.2%
e 2149
 
0.2%
i 1828
 
0.2%
321
 
< 0.1%
Other values (2) 642
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 983658
> 99.9%
Space Separator 321
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 162922
16.6%
o 162922
16.6%
r 162922
16.6%
l 162922
16.6%
m 162601
16.5%
a 162601
16.5%
t 2149
 
0.2%
e 2149
 
0.2%
i 1828
 
0.2%
s 321
 
< 0.1%
Space Separator
ValueCountFrequency (%)
321
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 983658
> 99.9%
Common 321
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 162922
16.6%
o 162922
16.6%
r 162922
16.6%
l 162922
16.6%
m 162601
16.5%
a 162601
16.5%
t 2149
 
0.2%
e 2149
 
0.2%
i 1828
 
0.2%
s 321
 
< 0.1%
Common
ValueCountFrequency (%)
321
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 983979
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 162922
16.6%
o 162922
16.6%
r 162922
16.6%
l 162922
16.6%
m 162601
16.5%
a 162601
16.5%
t 2149
 
0.2%
e 2149
 
0.2%
i 1828
 
0.2%
321
 
< 0.1%
Other values (2) 642
 
0.1%

dl_applied
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 MiB
0
161542 
1
 
3208

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters164750
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 161542
98.1%
1 3208
 
1.9%

Length

2025-05-23T23:07:21.916013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-23T23:07:22.011353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 161542
98.1%
1 3208
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 161542
98.1%
1 3208
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 164750
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 161542
98.1%
1 3208
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 164750
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 161542
98.1%
1 3208
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 164750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 161542
98.1%
1 3208
 
1.9%

winner
Categorical

Distinct13
Distinct (%)< 0.1%
Missing321
Missing (%)0.2%
Memory size13.1 MiB
Mumbai Indians
23634 
Chennai Super Kings
21692 
Kolkata Knight Riders
20213 
Royal Challengers Bangalore
18317 
Kings XI Punjab
18003 
Other values (8)
62570 

Length

Max length27
Median length21
Mean length18.093852
Min length13

Characters and Unicode

Total characters2975154
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSunrisers Hyderabad
2nd rowSunrisers Hyderabad
3rd rowSunrisers Hyderabad
4th rowSunrisers Hyderabad
5th rowSunrisers Hyderabad

Common Values

ValueCountFrequency (%)
Mumbai Indians 23634
14.3%
Chennai Super Kings 21692
13.2%
Kolkata Knight Riders 20213
12.3%
Royal Challengers Bangalore 18317
11.1%
Kings XI Punjab 18003
10.9%
Rajasthan Royals 16732
10.2%
Delhi Daredevils 15709
9.5%
Sunrisers Hyderabad 12360
7.5%
Deccan Chargers 7013
 
4.3%
Rising Pune Supergiants 3488
 
2.1%
Other values (3) 7268
 
4.4%

Length

2025-05-23T23:07:22.089748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kings 39695
 
9.6%
mumbai 23634
 
5.7%
indians 23634
 
5.7%
chennai 21692
 
5.3%
super 21692
 
5.3%
kolkata 20213
 
4.9%
knight 20213
 
4.9%
riders 20213
 
4.9%
royal 18317
 
4.4%
challengers 18317
 
4.4%
Other values (20) 184273
44.7%

Most occurring characters

ValueCountFrequency (%)
a 337181
 
11.3%
n 257712
 
8.7%
247464
 
8.3%
e 216924
 
7.3%
i 210591
 
7.1%
s 198331
 
6.7%
r 163198
 
5.5%
l 142953
 
4.8%
g 110531
 
3.7%
h 100998
 
3.4%
Other values (27) 989271
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2297794
77.2%
Uppercase Letter 429896
 
14.4%
Space Separator 247464
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 337181
14.7%
n 257712
11.2%
e 216924
9.4%
i 210591
9.2%
s 198331
8.6%
r 163198
 
7.1%
l 142953
 
6.2%
g 110531
 
4.8%
h 100998
 
4.4%
u 89933
 
3.9%
Other values (11) 469442
20.4%
Uppercase Letter
ValueCountFrequency (%)
K 82765
19.3%
R 75482
17.6%
C 47022
10.9%
I 41637
9.7%
D 38431
8.9%
S 37540
8.7%
P 24374
 
5.7%
M 23634
 
5.5%
B 18317
 
4.3%
X 18003
 
4.2%
Other values (5) 22691
 
5.3%
Space Separator
ValueCountFrequency (%)
247464
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2727690
91.7%
Common 247464
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 337181
 
12.4%
n 257712
 
9.4%
e 216924
 
8.0%
i 210591
 
7.7%
s 198331
 
7.3%
r 163198
 
6.0%
l 142953
 
5.2%
g 110531
 
4.1%
h 100998
 
3.7%
u 89933
 
3.3%
Other values (26) 899338
33.0%
Common
ValueCountFrequency (%)
247464
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2975154
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 337181
 
11.3%
n 257712
 
8.7%
247464
 
8.3%
e 216924
 
7.3%
i 210591
 
7.1%
s 198331
 
6.7%
r 163198
 
5.5%
l 142953
 
4.8%
g 110531
 
3.7%
h 100998
 
3.4%
Other values (27) 989271
33.3%

win_by_runs
Real number (ℝ)

Zeros 

Distinct88
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.589663
Minimum0
Maximum146
Zeros88526
Zeros (%)53.7%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2025-05-23T23:07:22.201239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q319
95-th percentile65
Maximum146
Range146
Interquartile range (IQR)19

Descriptive statistics

Standard deviation23.381121
Coefficient of variation (CV)1.7205077
Kurtosis7.4075351
Mean13.589663
Median Absolute Deviation (MAD)0
Skewness2.5041707
Sum2238897
Variance546.67681
MonotonicityNot monotonic
2025-05-23T23:07:22.311904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 88526
53.7%
14 2743
 
1.7%
4 2669
 
1.6%
10 2308
 
1.4%
9 2246
 
1.4%
7 2237
 
1.4%
13 2226
 
1.4%
15 2223
 
1.3%
23 2201
 
1.3%
1 1979
 
1.2%
Other values (78) 55392
33.6%
ValueCountFrequency (%)
0 88526
53.7%
1 1979
 
1.2%
2 1731
 
1.1%
3 1169
 
0.7%
4 2669
 
1.6%
5 1447
 
0.9%
6 1357
 
0.8%
7 2237
 
1.4%
8 1007
 
0.6%
9 2246
 
1.4%
ValueCountFrequency (%)
146 211
0.1%
144 242
0.1%
140 225
0.1%
138 212
0.1%
130 251
0.2%
111 232
0.1%
105 223
0.1%
102 234
0.1%
98 230
0.1%
97 474
0.3%

win_by_wickets
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2541608
Minimum0
Maximum10
Zeros78373
Zeros (%)47.6%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2025-05-23T23:07:22.416453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q36
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.3687539
Coefficient of variation (CV)1.0352143
Kurtosis-1.4969611
Mean3.2541608
Median Absolute Deviation (MAD)3
Skewness0.312933
Sum536123
Variance11.348503
MonotonicityNot monotonic
2025-05-23T23:07:22.500060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 78373
47.6%
6 17902
 
10.9%
7 17218
 
10.5%
5 15269
 
9.3%
8 11277
 
6.8%
4 8936
 
5.4%
9 7755
 
4.7%
3 3958
 
2.4%
10 2103
 
1.3%
2 1222
 
0.7%
ValueCountFrequency (%)
0 78373
47.6%
1 737
 
0.4%
2 1222
 
0.7%
3 3958
 
2.4%
4 8936
 
5.4%
5 15269
 
9.3%
6 17902
 
10.9%
7 17218
 
10.5%
8 11277
 
6.8%
9 7755
 
4.7%
ValueCountFrequency (%)
10 2103
 
1.3%
9 7755
4.7%
8 11277
6.8%
7 17218
10.5%
6 17902
10.9%
5 15269
9.3%
4 8936
5.4%
3 3958
 
2.4%
2 1222
 
0.7%
1 737
 
0.4%
Distinct214
Distinct (%)0.1%
Missing321
Missing (%)0.2%
Memory size11.7 MiB
2025-05-23T23:07:22.801978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length17
Median length16
Mean length9.461567
Min length5

Characters and Unicode

Total characters1555756
Distinct characters53
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYuvraj Singh
2nd rowYuvraj Singh
3rd rowYuvraj Singh
4th rowYuvraj Singh
5th rowYuvraj Singh
ValueCountFrequency (%)
sharma 6685
 
2.0%
a 5923
 
1.8%
ch 5176
 
1.5%
de 5148
 
1.5%
sr 4989
 
1.5%
v 4954
 
1.5%
sk 4826
 
1.4%
ab 4806
 
1.4%
gayle 4697
 
1.4%
villiers 4417
 
1.3%
Other values (328) 284074
84.6%
2025-05-23T23:07:23.216927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 171890
 
11.0%
171266
 
11.0%
e 75492
 
4.9%
n 73668
 
4.7%
i 72718
 
4.7%
r 72126
 
4.6%
h 69358
 
4.5%
l 64960
 
4.2%
S 60858
 
3.9%
s 44596
 
2.9%
Other values (43) 678824
43.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 915456
58.8%
Uppercase Letter 468237
30.1%
Space Separator 171266
 
11.0%
Dash Punctuation 797
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 171890
18.8%
e 75492
 
8.2%
n 73668
 
8.0%
i 72718
 
7.9%
r 72126
 
7.9%
h 69358
 
7.6%
l 64960
 
7.1%
s 44596
 
4.9%
t 34664
 
3.8%
o 34341
 
3.8%
Other values (16) 201643
22.0%
Uppercase Letter
ValueCountFrequency (%)
S 60858
13.0%
M 41878
 
8.9%
A 41527
 
8.9%
R 37131
 
7.9%
K 35618
 
7.6%
P 30987
 
6.6%
D 25955
 
5.5%
J 23648
 
5.1%
G 22996
 
4.9%
H 21886
 
4.7%
Other values (15) 125753
26.9%
Space Separator
ValueCountFrequency (%)
171266
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 797
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1383693
88.9%
Common 172063
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 171890
 
12.4%
e 75492
 
5.5%
n 73668
 
5.3%
i 72718
 
5.3%
r 72126
 
5.2%
h 69358
 
5.0%
l 64960
 
4.7%
S 60858
 
4.4%
s 44596
 
3.2%
M 41878
 
3.0%
Other values (41) 636149
46.0%
Common
ValueCountFrequency (%)
171266
99.5%
- 797
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1555756
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 171890
 
11.0%
171266
 
11.0%
e 75492
 
4.9%
n 73668
 
4.7%
i 72718
 
4.7%
r 72126
 
4.6%
h 69358
 
4.5%
l 64960
 
4.2%
S 60858
 
3.9%
s 44596
 
2.9%
Other values (43) 678824
43.6%

venue
Categorical

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.2 MiB
M Chinnaswamy Stadium
16872 
Eden Gardens
16268 
Wankhede Stadium
15879 
Feroz Shah Kotla
15604 
Rajiv Gandhi International Stadium, Uppal
13303 
Other values (29)
86824 

Length

Max length52
Median length44
Mean length25.466197
Min length8

Characters and Unicode

Total characters4195556
Distinct characters53
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRajiv Gandhi International Stadium, Uppal
2nd rowRajiv Gandhi International Stadium, Uppal
3rd rowRajiv Gandhi International Stadium, Uppal
4th rowRajiv Gandhi International Stadium, Uppal
5th rowRajiv Gandhi International Stadium, Uppal

Common Values

ValueCountFrequency (%)
M Chinnaswamy Stadium 16872
 
10.2%
Eden Gardens 16268
 
9.9%
Wankhede Stadium 15879
 
9.6%
Feroz Shah Kotla 15604
 
9.5%
Rajiv Gandhi International Stadium, Uppal 13303
 
8.1%
MA Chidambaram Stadium, Chepauk 12006
 
7.3%
Punjab Cricket Association IS Bindra Stadium, Mohali 11581
 
7.0%
Sawai Mansingh Stadium 9478
 
5.8%
Maharashtra Cricket Association Stadium 5055
 
3.1%
Subrata Roy Sahara Stadium 4064
 
2.5%
Other values (24) 44640
27.1%

Length

2025-05-23T23:07:23.357916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
stadium 116298
20.3%
cricket 29568
 
5.2%
association 21918
 
3.8%
international 18105
 
3.2%
m 16872
 
2.9%
chinnaswamy 16872
 
2.9%
gardens 16268
 
2.8%
eden 16268
 
2.8%
wankhede 15879
 
2.8%
feroz 15604
 
2.7%
Other values (64) 289207
50.5%

Most occurring characters

ValueCountFrequency (%)
a 547668
 
13.1%
408109
 
9.7%
i 323122
 
7.7%
t 251850
 
6.0%
n 228663
 
5.5%
d 227385
 
5.4%
e 192866
 
4.6%
S 187907
 
4.5%
r 184959
 
4.4%
m 170116
 
4.1%
Other values (43) 1472911
35.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3107921
74.1%
Uppercase Letter 627140
 
14.9%
Space Separator 408109
 
9.7%
Other Punctuation 49840
 
1.2%
Dash Punctuation 2546
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 547668
17.6%
i 323122
10.4%
t 251850
 
8.1%
n 228663
 
7.4%
d 227385
 
7.3%
e 192866
 
6.2%
r 184959
 
6.0%
m 170116
 
5.5%
h 158810
 
5.1%
u 155985
 
5.0%
Other values (15) 666497
21.4%
Uppercase Letter
ValueCountFrequency (%)
S 187907
30.0%
C 78886
12.6%
M 57885
 
9.2%
A 47226
 
7.5%
G 32169
 
5.1%
I 29686
 
4.7%
P 26805
 
4.3%
R 22459
 
3.6%
K 19247
 
3.1%
W 17819
 
2.8%
Other values (13) 107051
17.1%
Other Punctuation
ValueCountFrequency (%)
, 40525
81.3%
. 7638
 
15.3%
' 1677
 
3.4%
Space Separator
ValueCountFrequency (%)
408109
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2546
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3735061
89.0%
Common 460495
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 547668
14.7%
i 323122
 
8.7%
t 251850
 
6.7%
n 228663
 
6.1%
d 227385
 
6.1%
e 192866
 
5.2%
S 187907
 
5.0%
r 184959
 
5.0%
m 170116
 
4.6%
h 158810
 
4.3%
Other values (38) 1261715
33.8%
Common
ValueCountFrequency (%)
408109
88.6%
, 40525
 
8.8%
. 7638
 
1.7%
- 2546
 
0.6%
' 1677
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4195556
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 547668
 
13.1%
408109
 
9.7%
i 323122
 
7.7%
t 251850
 
6.0%
n 228663
 
5.5%
d 227385
 
5.4%
e 192866
 
4.6%
S 187907
 
4.5%
r 184959
 
4.4%
m 170116
 
4.1%
Other values (43) 1472911
35.1%
Distinct55
Distinct (%)< 0.1%
Missing248
Missing (%)0.2%
Memory size11.9 MiB
2025-05-23T23:07:23.547670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length21
Median length20
Mean length10.663305
Min length5

Characters and Unicode

Total characters1754135
Distinct characters46
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAY Dandekar
2nd rowAY Dandekar
3rd rowAY Dandekar
4th rowAY Dandekar
5th rowAY Dandekar
ValueCountFrequency (%)
dharmasena 18930
 
5.7%
hdpk 17486
 
5.3%
s 16605
 
5.0%
asad 12010
 
3.6%
rauf 12010
 
3.6%
chaudhary 11292
 
3.4%
ak 10104
 
3.0%
ravi 9612
 
2.9%
erasmus 9472
 
2.9%
dar 9137
 
2.8%
Other values (85) 205186
61.8%
2025-05-23T23:07:23.864526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 218580
 
12.5%
167342
 
9.5%
e 112138
 
6.4%
n 109956
 
6.3%
r 103788
 
5.9%
s 72020
 
4.1%
D 69575
 
4.0%
h 68442
 
3.9%
d 56007
 
3.2%
o 55133
 
3.1%
Other values (36) 721154
41.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1121368
63.9%
Uppercase Letter 465425
26.5%
Space Separator 167342
 
9.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 218580
19.5%
e 112138
10.0%
n 109956
9.8%
r 103788
9.3%
s 72020
 
6.4%
h 68442
 
6.1%
d 56007
 
5.0%
o 55133
 
4.9%
i 46793
 
4.2%
m 45931
 
4.1%
Other values (14) 232580
20.7%
Uppercase Letter
ValueCountFrequency (%)
D 69575
14.9%
A 52175
11.2%
K 50929
10.9%
R 42840
9.2%
B 40702
8.7%
S 30489
 
6.6%
H 26063
 
5.6%
C 24123
 
5.2%
P 19133
 
4.1%
M 18035
 
3.9%
Other values (11) 91361
19.6%
Space Separator
ValueCountFrequency (%)
167342
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1586793
90.5%
Common 167342
 
9.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 218580
 
13.8%
e 112138
 
7.1%
n 109956
 
6.9%
r 103788
 
6.5%
s 72020
 
4.5%
D 69575
 
4.4%
h 68442
 
4.3%
d 56007
 
3.5%
o 55133
 
3.5%
A 52175
 
3.3%
Other values (35) 668979
42.2%
Common
ValueCountFrequency (%)
167342
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1754135
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 218580
 
12.5%
167342
 
9.5%
e 112138
 
6.4%
n 109956
 
6.3%
r 103788
 
5.9%
s 72020
 
4.1%
D 69575
 
4.0%
h 68442
 
3.9%
d 56007
 
3.2%
o 55133
 
3.1%
Other values (36) 721154
41.1%
Distinct58
Distinct (%)< 0.1%
Missing248
Missing (%)0.2%
Memory size11.8 MiB
2025-05-23T23:07:24.359521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length21
Median length16
Mean length10.292702
Min length5

Characters and Unicode

Total characters1693170
Distinct characters49
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNJ Llong
2nd rowNJ Llong
3rd rowNJ Llong
4th rowNJ Llong
5th rowNJ Llong
ValueCountFrequency (%)
s 17360
 
5.2%
ravi 13475
 
4.1%
sja 12981
 
3.9%
taufel 12981
 
3.9%
c 11924
 
3.6%
shamshuddin 11924
 
3.6%
nandan 9327
 
2.8%
tucker 8925
 
2.7%
rj 8779
 
2.6%
ck 8350
 
2.5%
Other values (88) 216472
65.1%
2025-05-23T23:07:24.701680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 181566
 
10.7%
167996
 
9.9%
n 105646
 
6.2%
r 99453
 
5.9%
e 89341
 
5.3%
i 82637
 
4.9%
S 72914
 
4.3%
h 70688
 
4.2%
u 56992
 
3.4%
d 52433
 
3.1%
Other values (39) 713504
42.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1058915
62.5%
Uppercase Letter 464860
27.5%
Space Separator 167996
 
9.9%
Other Punctuation 1399
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 181566
17.1%
n 105646
10.0%
r 99453
9.4%
e 89341
 
8.4%
i 82637
 
7.8%
h 70688
 
6.7%
u 56992
 
5.4%
d 52433
 
5.0%
f 42190
 
4.0%
s 41084
 
3.9%
Other values (15) 236885
22.4%
Uppercase Letter
ValueCountFrequency (%)
S 72914
15.7%
K 48947
10.5%
A 42922
9.2%
R 42665
9.2%
J 37323
8.0%
T 36419
 
7.8%
C 28377
 
6.1%
N 23976
 
5.2%
B 20046
 
4.3%
D 19561
 
4.2%
Other values (12) 91710
19.7%
Space Separator
ValueCountFrequency (%)
167996
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1399
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1523775
90.0%
Common 169395
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 181566
 
11.9%
n 105646
 
6.9%
r 99453
 
6.5%
e 89341
 
5.9%
i 82637
 
5.4%
S 72914
 
4.8%
h 70688
 
4.6%
u 56992
 
3.7%
d 52433
 
3.4%
K 48947
 
3.2%
Other values (37) 663158
43.5%
Common
ValueCountFrequency (%)
167996
99.2%
. 1399
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1693170
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 181566
 
10.7%
167996
 
9.9%
n 105646
 
6.2%
r 99453
 
5.9%
e 89341
 
5.3%
i 82637
 
4.9%
S 72914
 
4.3%
h 70688
 
4.2%
u 56992
 
3.4%
d 52433
 
3.1%
Other values (39) 713504
42.1%

Interactions

2025-05-23T23:07:10.420337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:06:56.448074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:06:57.791359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:06:59.113813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:00.507541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:02.536722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:03.785612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:05.120315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:06.400023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:07.859707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:09.114259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:10.561328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:06:56.560969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:06:57.924522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:06:59.242522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:00.663261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:02.655116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:03.910529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:05.256223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:06.516369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:07.974189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:09.241448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:10.659924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:06:56.692820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:06:58.039582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:06:59.356337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:01.420146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:02.781846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:04.047080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:05.370382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:06.633507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:08.095746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:09.364165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:10.789630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:06:56.825448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:06:58.176976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:06:59.476311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:01.554482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:02.903446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:04.168453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:05.484372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:06.766067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:08.202882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:09.487445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:10.899977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:06:56.941965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:06:58.290656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:06:59.606592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:01.670900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:03.011160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:04.269070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:05.603826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:07.049501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:08.302212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:09.599124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:11.003630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:06:57.058971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:06:58.407647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:06:59.706044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:01.787395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:03.117413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:04.385702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:05.702992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:07.173582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:08.425056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:09.711152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:11.116225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:06:57.178891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:06:58.532490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:06:59.827199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:01.920998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:03.219467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:04.519944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:05.821606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:07.290430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:08.539341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:09.828964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:11.237033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:06:57.300210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:06:58.651605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:06:59.947629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:02.060593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:03.339947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:04.634753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:05.937487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:07.406501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:08.651968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:09.949480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:11.345691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:06:57.441284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:06:58.760991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:00.080515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:02.180419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:03.444324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:04.741104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:06.054540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:07.515416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:08.775619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:10.062952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:11.447040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:06:57.556662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:06:58.873896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:00.226513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:02.309608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:03.553070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:04.859713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:06.167050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:07.627896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:08.881111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:10.166472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:11.561999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:06:57.678137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:06:58.998636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:00.379772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:02.428411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:03.676281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:04.980067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:06.283234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:07.748500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:09.009299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-23T23:07:10.301784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Missing values

2025-05-23T23:07:11.808325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-23T23:07:12.535554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-23T23:07:13.384663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

match_idinningbatting_teambowling_teamoverballbatsmannon_strikerbowleris_super_overwide_runsbye_runslegbye_runsnoball_runspenalty_runsbatsman_runsextra_runstotal_runsplayer_dismisseddismissal_kindfielderseasoncitydateteam1team2toss_winnertoss_decisionresultdl_appliedwinnerwin_by_runswin_by_wicketsplayer_of_matchvenueumpire1umpire2
id
111Sunrisers HyderabadRoyal Challengers Bangalore11DA WarnerS DhawanTS Mills000000000NaNNaNNaN2017Hyderabad2017-04-05Sunrisers HyderabadRoyal Challengers BangaloreRoyal Challengers Bangalorefieldnormal0Sunrisers Hyderabad350Yuvraj SinghRajiv Gandhi International Stadium, UppalAY DandekarNJ Llong
111Sunrisers HyderabadRoyal Challengers Bangalore12DA WarnerS DhawanTS Mills000000000NaNNaNNaN2017Hyderabad2017-04-05Sunrisers HyderabadRoyal Challengers BangaloreRoyal Challengers Bangalorefieldnormal0Sunrisers Hyderabad350Yuvraj SinghRajiv Gandhi International Stadium, UppalAY DandekarNJ Llong
111Sunrisers HyderabadRoyal Challengers Bangalore13DA WarnerS DhawanTS Mills000000404NaNNaNNaN2017Hyderabad2017-04-05Sunrisers HyderabadRoyal Challengers BangaloreRoyal Challengers Bangalorefieldnormal0Sunrisers Hyderabad350Yuvraj SinghRajiv Gandhi International Stadium, UppalAY DandekarNJ Llong
111Sunrisers HyderabadRoyal Challengers Bangalore14DA WarnerS DhawanTS Mills000000000NaNNaNNaN2017Hyderabad2017-04-05Sunrisers HyderabadRoyal Challengers BangaloreRoyal Challengers Bangalorefieldnormal0Sunrisers Hyderabad350Yuvraj SinghRajiv Gandhi International Stadium, UppalAY DandekarNJ Llong
111Sunrisers HyderabadRoyal Challengers Bangalore15DA WarnerS DhawanTS Mills020000022NaNNaNNaN2017Hyderabad2017-04-05Sunrisers HyderabadRoyal Challengers BangaloreRoyal Challengers Bangalorefieldnormal0Sunrisers Hyderabad350Yuvraj SinghRajiv Gandhi International Stadium, UppalAY DandekarNJ Llong
111Sunrisers HyderabadRoyal Challengers Bangalore16S DhawanDA WarnerTS Mills000000000NaNNaNNaN2017Hyderabad2017-04-05Sunrisers HyderabadRoyal Challengers BangaloreRoyal Challengers Bangalorefieldnormal0Sunrisers Hyderabad350Yuvraj SinghRajiv Gandhi International Stadium, UppalAY DandekarNJ Llong
111Sunrisers HyderabadRoyal Challengers Bangalore17S DhawanDA WarnerTS Mills000100011NaNNaNNaN2017Hyderabad2017-04-05Sunrisers HyderabadRoyal Challengers BangaloreRoyal Challengers Bangalorefieldnormal0Sunrisers Hyderabad350Yuvraj SinghRajiv Gandhi International Stadium, UppalAY DandekarNJ Llong
111Sunrisers HyderabadRoyal Challengers Bangalore21S DhawanDA WarnerA Choudhary000000101NaNNaNNaN2017Hyderabad2017-04-05Sunrisers HyderabadRoyal Challengers BangaloreRoyal Challengers Bangalorefieldnormal0Sunrisers Hyderabad350Yuvraj SinghRajiv Gandhi International Stadium, UppalAY DandekarNJ Llong
111Sunrisers HyderabadRoyal Challengers Bangalore22DA WarnerS DhawanA Choudhary000000404NaNNaNNaN2017Hyderabad2017-04-05Sunrisers HyderabadRoyal Challengers BangaloreRoyal Challengers Bangalorefieldnormal0Sunrisers Hyderabad350Yuvraj SinghRajiv Gandhi International Stadium, UppalAY DandekarNJ Llong
111Sunrisers HyderabadRoyal Challengers Bangalore23DA WarnerS DhawanA Choudhary000010011NaNNaNNaN2017Hyderabad2017-04-05Sunrisers HyderabadRoyal Challengers BangaloreRoyal Challengers Bangalorefieldnormal0Sunrisers Hyderabad350Yuvraj SinghRajiv Gandhi International Stadium, UppalAY DandekarNJ Llong
match_idinningbatting_teambowling_teamoverballbatsmannon_strikerbowleris_super_overwide_runsbye_runslegbye_runsnoball_runspenalty_runsbatsman_runsextra_runstotal_runsplayer_dismisseddismissal_kindfielderseasoncitydateteam1team2toss_winnertoss_decisionresultdl_appliedwinnerwin_by_runswin_by_wicketsplayer_of_matchvenueumpire1umpire2
id
795379532Chennai Super KingsSunrisers Hyderabad176SR WatsonAT RayuduRashid Khan000000404NaNNaNNaN2018Mumbai27/05/18Sunrisers HyderabadChennai Super KingsChennai Super Kingsfieldnormal0Chennai Super Kings08SR WatsonWankhede StadiumMarais ErasmusS Ravi
795379532Chennai Super KingsSunrisers Hyderabad181AT RayuduSR WatsonS Kaul000000101NaNNaNNaN2018Mumbai27/05/18Sunrisers HyderabadChennai Super KingsChennai Super Kingsfieldnormal0Chennai Super Kings08SR WatsonWankhede StadiumMarais ErasmusS Ravi
795379532Chennai Super KingsSunrisers Hyderabad182SR WatsonAT RayuduS Kaul000000101NaNNaNNaN2018Mumbai27/05/18Sunrisers HyderabadChennai Super KingsChennai Super Kingsfieldnormal0Chennai Super Kings08SR WatsonWankhede StadiumMarais ErasmusS Ravi
795379532Chennai Super KingsSunrisers Hyderabad183AT RayuduSR WatsonS Kaul000000101NaNNaNNaN2018Mumbai27/05/18Sunrisers HyderabadChennai Super KingsChennai Super Kingsfieldnormal0Chennai Super Kings08SR WatsonWankhede StadiumMarais ErasmusS Ravi
795379532Chennai Super KingsSunrisers Hyderabad184SR WatsonAT RayuduS Kaul000000404NaNNaNNaN2018Mumbai27/05/18Sunrisers HyderabadChennai Super KingsChennai Super Kingsfieldnormal0Chennai Super Kings08SR WatsonWankhede StadiumMarais ErasmusS Ravi
795379532Chennai Super KingsSunrisers Hyderabad185SR WatsonAT RayuduS Kaul000000404NaNNaNNaN2018Mumbai27/05/18Sunrisers HyderabadChennai Super KingsChennai Super Kingsfieldnormal0Chennai Super Kings08SR WatsonWankhede StadiumMarais ErasmusS Ravi
795379532Chennai Super KingsSunrisers Hyderabad186SR WatsonAT RayuduS Kaul000000000NaNNaNNaN2018Mumbai27/05/18Sunrisers HyderabadChennai Super KingsChennai Super Kingsfieldnormal0Chennai Super Kings08SR WatsonWankhede StadiumMarais ErasmusS Ravi
795379532Chennai Super KingsSunrisers Hyderabad191AT RayuduSR WatsonCR Brathwaite000000000NaNNaNNaN2018Mumbai27/05/18Sunrisers HyderabadChennai Super KingsChennai Super Kingsfieldnormal0Chennai Super Kings08SR WatsonWankhede StadiumMarais ErasmusS Ravi
795379532Chennai Super KingsSunrisers Hyderabad192AT RayuduSR WatsonCR Brathwaite000000000NaNNaNNaN2018Mumbai27/05/18Sunrisers HyderabadChennai Super KingsChennai Super Kingsfieldnormal0Chennai Super Kings08SR WatsonWankhede StadiumMarais ErasmusS Ravi
795379532Chennai Super KingsSunrisers Hyderabad193AT RayuduSR WatsonCR Brathwaite000000404NaNNaNNaN2018Mumbai27/05/18Sunrisers HyderabadChennai Super KingsChennai Super Kingsfieldnormal0Chennai Super Kings08SR WatsonWankhede StadiumMarais ErasmusS Ravi

Duplicate rows

Most frequently occurring

match_idinningbatting_teambowling_teamoverballbatsmannon_strikerbowleris_super_overwide_runsbye_runslegbye_runsnoball_runspenalty_runsbatsman_runsextra_runstotal_runsplayer_dismisseddismissal_kindfielderseasoncitydateteam1team2toss_winnertoss_decisionresultdl_appliedwinnerwin_by_runswin_by_wicketsplayer_of_matchvenueumpire1umpire2# duplicates
02211Mumbai IndiansDelhi Daredevils41SR TendulkarC MadanPJ Sangwan000000101NaNNaNNaN2010Mumbai2010-04-13Mumbai IndiansDelhi DaredevilsMumbai Indiansbatnormal0Mumbai Indians390KA PollardBrabourne StadiumS AsnaniDJ Harper2
179461Rajasthan RoyalsRoyal Challengers Bangalore44AM RahaneRA TripathiUT Yadav000000404NaNNaNNaN2018Jaipur19/05/18Rajasthan RoyalsRoyal Challengers BangaloreRajasthan Royalsbatnormal0Rajasthan Royals300S GopalSawai Mansingh StadiumBruce OxenfordVirender Kumar Sharma2
279461Rajasthan RoyalsRoyal Challengers Bangalore45AM RahaneRA TripathiUT Yadav000000101NaNNaNNaN2018Jaipur19/05/18Rajasthan RoyalsRoyal Challengers BangaloreRajasthan Royalsbatnormal0Rajasthan Royals300S GopalSawai Mansingh StadiumBruce OxenfordVirender Kumar Sharma2
379461Rajasthan RoyalsRoyal Challengers Bangalore135RA TripathiAM RahaneYS Chahal000000000NaNNaNNaN2018Jaipur19/05/18Rajasthan RoyalsRoyal Challengers BangaloreRajasthan Royalsbatnormal0Rajasthan Royals300S GopalSawai Mansingh StadiumBruce OxenfordVirender Kumar Sharma2
479462Royal Challengers BangaloreRajasthan Royals101AB de VilliersMandeep SinghI Sodhi000000000NaNNaNNaN2018Jaipur19/05/18Rajasthan RoyalsRoyal Challengers BangaloreRajasthan Royalsbatnormal0Rajasthan Royals300S GopalSawai Mansingh StadiumBruce OxenfordVirender Kumar Sharma2